RDF Data Management

图形数据在语义网、社会网络分析、生物信息学和物理通信网络等许多应用中越来越重要。图自然地模拟了这些领域的复杂结构，例如社会网络或蛋白质-蛋白质相互作用网络中人与人之间的关系。这些图形数据的规模和复杂性给数据管理和数据分析带来了重大挑战。我广泛的研究范围就是对这些问题的研究。在这个发现项目中，我的重点是来自Web资源模型的图结构。资源描述框架（RDF）是一个标准（由W3C提出），Web对象通常通过它来建模。RDF是一种自描述数据模型，适合于机器理解和解释，因此有望促进“语义web”。W3C还定义了一种名为SPARQL的查询语言，用于访问RDF存储库。RDF数据集已经开始激增和增长。例如，Yago和DBPedia从Wikipedia中提取事实并以RDF格式存储，以方便对Wikipedia的结构化查询；作为电子政务计划的一部分，许多地方政府现在正在用RDF格式对他们提供给公民的资源进行编码；生物学家已经建立了精细的RDF数据集合（BioRDF和Uniprot RDF），用于实验数据的社区共享；链接开放数据（LOD）作为一个网络数据集成平台正在不断发展壮大（截至2011年9月——这是最新的可用信息——超过3100万个三元组）。因此，管理和分析大型分布式RDF数据集已成为一个紧迫而重要的问题。我们小组的RDF数据管理和分析方法不同于许多现有的方法，这些方法以某种方式将RDF映射为关系表示，并将SPARQL查询转换为SQL。尽管这具有利用成熟技术的优势，但它会产生性能和建模不匹配的问题。我们将RDF数据集建模为图（这是RDF的本机模型），并将SPARQL查询表示为图。因此，查询执行简化为图匹配。这种方法具有建模和性能方面的优势。在这一总体方针下，我打算在未来五年内研究以下问题：RDF图的高效存储结构。用于SPARQL查询（包括聚合查询，它现在是SPARQL标准的一部分）的高效和有效的查询处理和优化技术。RDF图的分布和对分布式RDF存储的SPARQL查询的评估。使用RDF查询和集成Web数据，这需要对RDF数据有一定的推理能力（所谓的owl2蕴涵机制）。将遵循的方法论包括算法研究、原型系统的开发和广泛的实验。这项研究的成功完成将导致RDF数据管理以及通过RDF进行web数据集成和查询的高效技术的发展。由于RDF技术现在被广泛部署（包括许多国家的各级政府），其结果将在技术和社会方面产生重大影响。

Graph data are of growing importance in many applications including the semantic web, social network analysis, bioinformatics, and physical communication networks. Graphs naturally model complicated structures in these fields, such as the relationships among people in a social network or the protein-protein interaction networks. The size and complexity of these graph data raise significant data management and data analysis challenges. My broad research scope is the study of these problems.In this discovery grant, my focus is on the graph structures that arise from models of Web resources. The Resource Description Framework (RDF) is the standard (proposed by W3C) by which Web objects are commonly modeled. RDF is a self-descriptive data model that is suitable for machine understanding and interpretation, and, therefore, expected to facilitate the “semantic web”. W3C has also defined a query language, called SPARQL, for accessing RDF repositories. RDF data sets have started to proliferate and grow. For example, Yago and DBPedia extract facts from Wikipedia and store them in RDF format to facilitate structural queries over Wikipedia; many local governments are now encoding the resources they provide to citizens in RDF format as part of the e-government initiatives; biologists have built elaborate RDF data collections (BioRDF and Uniprot RDF) for community sharing of experimental data; and Linked Open Data (LOD) initiative has been growing (as of September 2011 - which are the latest available information - over 31 million triples [tuples]) as a web data integration platform. Consequently, managing and analyzing large and distributed RDF datasets have emerged as an urgent and important concern.My group’s approach to RDF data management and analysis differs from many of the existing approaches that map, in one way or another, RDF into a relational representation and convert SPARQL queries into SQL. Although this has the advantage of leveraging mature technology, it gives rise to performance and modeling mismatch problems. We model an RDF dataset as a graph (which is the native model for RDF) and also represent a SPARQL query as a graph. Consequently, query execution reduces to graph matching. This approach has modeling and performance advantages. Within this general approach, I intend to study the following issues over the next five years:1. Efficient storage structures for RDF graphs.2. Efficient and effective query processing and optimization techniques for SPARQL queries (including aggregation queries that are now part of the SPARQL standard).3. Distribution of RDF graphs and evaluation of SPARQL queries over distributed RDF stores.4. Web data querying and integration using RDF, which requires some reasoning capability over RDF data (so called OWL 2 entailment regime).The methodology that will be followed includes algorithmic studies, development of prototype systems, and extensive experimentation.Successful completion of this research will result in the development of efficient and effective techniques for RDF data management, and web data integration and querying through RDF. Since RDF technology is now widely deployed (including by various levels of government at a number of countries), the results will have significant impact both technically and societally.